AI Material Mechanics Assistant (Merged)
Overview
AI Material Mechanics Assistant (Merged) is a Chinese-language assistant for mechanics-of-materials question answering, developed through LoRA-based instruction fine-tuning of a Qwen2.5-7B-family model.
The project explores domain adaptation for explaining mechanical concepts, interpreting engineering formulas, and presenting worked solutions to textbook-style problems. Its intended role is educational assistance and exploratory research, rather than independently verified engineering analysis.
This repository distributes a complete BF16 checkpoint, together with configuration and tokenizer files in Hugging Face Transformers format.
Model Details
| Item | Description |
|---|---|
| Developer | Yanghao Chen / CYHcyh66 |
| Primary language | Chinese |
| Domain | Mechanics of materials and introductory engineering mechanics |
| Base model family | Qwen2.5-7B |
| Adaptation method | LoRA-based instruction fine-tuning |
| Architecture | Qwen2ForCausalLM |
| Transformer layers | 28 |
| Stored weight precision | BF16 |
| Release format | Four Safetensors weight shards, an index, configuration files, and tokenizer files |
The architecture and layer count are recorded in config.json. The weight layout is described in model.safetensors.index.json.
This repository contains complete model weights, not an adapter-only release. The exported configuration does not identify the exact upstream base-model repository or revision.
Associated Dataset
The associated public dataset is Material-mechanics-merge.
Its published file, material_zh_shujuji.json, contains 774 records with three fields:
instruction: The task description or guidance for the response.input: The mechanics question or additional context; this field can be an empty string.output: The accompanying explanation or worked answer.
The examples include:
- Explanations of stress, strain, strength, stiffness, and mechanical assumptions.
- Formula interpretation and worked calculations for axial loading, torsion, and beam bending.
- Stress analysis, principal stresses, and constitutive relations.
- Buckling, energy methods, and composite-member calculations.
- Chinese-language tutoring-style questions and explanations.
The 774-record count describes the published dataset file. It does not establish the number of unique problems or the exact examples used in a particular training run. The complete training selection and internal training–validation split are not documented in this model repository.
Dataset answers should be reviewed for mechanical correctness before being reused as evaluation references.
Fine-Tuning and Reproducibility
The model was adapted using LoRA-based instruction fine-tuning. The exported configuration records Unsloth 2025.3.19 and Transformers 4.50.3; these are recorded software versions, not a tested minimum-version specification.
The public repository contains the exported model but does not include a training script, LoRA configuration, or training logs. Exact training hyperparameters, hardware, and compute time are therefore not specified in this card. Their absence from the public release does not imply that they were never recorded elsewhere.
Usage Notes
The checkpoint and tokenizer are packaged for use with Hugging Face Transformers.
The published tokenizer_config.json does not define a chat template. The prompt format used during fine-tuning should be confirmed before adopting a generic chat-template inference example.
The context-length setting in the exported configuration should not be interpreted as evidence of validated long-context performance on mechanics tasks.
Evaluation
The public repository does not provide quantitative held-out evaluation results, an evaluation script, or a reproducible comparison against the base model.
Accordingly, this card makes no measured claim of improved mechanics accuracy or superior performance over the related model release.
Future evaluation should use questions excluded from training and assess:
- Conceptual correctness and recognition of mechanical assumptions.
- Appropriate formula selection.
- Numerical accuracy and unit consistency.
- Completeness and clarity of explanations.
Training examples and illustrative answers should not be presented as independent test results.
Intended Use and Limitations
The model is intended for Chinese-language educational assistance, such as reviewing mechanics concepts, discussing formula assumptions, and exploring explanations of textbook-style problems.
- Responses may contain incorrect formulas, arithmetic, units, or mechanical reasoning.
- Topic coverage in the associated dataset does not establish reliable performance across those topics.
- Important calculations should be independently checked against reliable references and calculations.
- The model does not replace finite-element analysis, experimental validation, or professional engineering judgment.
- It should not be used as the sole basis for safety-critical engineering decisions.
Related Resources
- This model: AI Material Mechanics Assistant (Merged)
- Associated dataset: Material-mechanics-merge — 774 published records.
- Related model: AI Material Mechanics Assistant
- Related dataset: Material-mechanics — 238 published records.
Both model repositories contain complete weight files. Their names and associated dataset sizes alone do not establish their training sequence, whether their weights are identical, or their relative performance.
Citation
If you use this model, please cite the repository:
@misc{chen2026_ai_material_mechanics_assistant_merged,
author = {Chen, Yanghao},
title = {AI Material Mechanics Assistant (Merged)},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/CYHcyh66/AI_Material_mechanics_assistant_merged}}
}
Contact
Please use the repository's Community tab for questions, feedback, or reports of incorrect mechanics explanations.